ImportYeti Scraper — Bills of Lading, Suppliers, HS Codes & Trade Lanes

Turn any ImportYeti company, supplier, HS-code, or country page into structured US import data: bill-of-lading rows with full counterparty addresses, supplier/customer league tables, HS-code breakdowns, and port-to-port trade lanes — in one actor, one run, one dataset.

How it works

✨ Why use this scraper?

• Four target types in one actor — US importers, foreign suppliers, HS codes (84, 8471, 847130), and country/location directories. Competing actors split these across separate paid products.
• Richer rows than the visible page — the actor reads the page's full embedded data payload, not the HTML table. You get ISO dates, complete counterparty street addresses, shipping routes, and estimated freight cost per shipment — fields the on-page table doesn't even render.
• Four record types, one dataset — bill_of_lading, summary, top_partner, and trade_lane records, each tagged for easy filtering. Turn on only what you need.
• Resume without re-paying — when maxItems truncates a run, a resumeCursor is written to the run OUTPUT. Paste it into the next run and the crawl continues exactly where it stopped.
• Built for Cloudflare — ImportYeti blocks datacenter IPs; this actor ships with residential US proxying and browser-grade TLS fingerprints by default. No CAPTCHAs to babysit.
• Honest scope — 50 most-recent shipments per target (that's what ImportYeti exposes publicly). No paywall bypass, no login, no fabricated fields.

🎯 Use cases

Who	What they do with it
Sourcing & procurement teams	Find every foreign factory shipping a product category to the US, with full addresses and shipment volumes
Competitive intelligence analysts	Map a competitor's supplier network, shipment cadence, and freight lanes
Sales & lead-gen teams	Build lists of active US importers per HS code or country, enriched with websites and shipment history
Trade & tariff researchers	Roll up shipments by HS chapter, origin country, and entry port to quantify tariff exposure
Logistics & freight forwarders	See which carriers (SCAC) and port pairs dominate a shipper's traffic, with TEU and estimated spend
Supply-chain risk teams	Check UFLPA flags, supplier concentration, and year-over-year volume shifts per partner

📥 Supported inputs

Input	Format	Example
companies	slug, path, or URL	apple, /company/walmart, https://www.importyeti.com/company/costco
suppliers	slug, path, or URL	foxconn-interconnect-technology
hsCodes	2/4/6-digit code or URL	84, 8471, 847130, https://www.importyeti.com/hs-codes/8471-automatic-data-processing-machines-and
locations	country slug or location URL	china, https://www.importyeti.com/location/company/united-states-of-america/state/4662168-tennessee
startUrls	any importyeti.com URL	company, supplier, HS-code, location, or /tags/... product-tag pages — auto-classified

Not supported: free-text search (disallowed by the site's robots.txt — paste the URLs you care about instead), per-BOL detail pages behind ImportYeti's premium tier, and contact emails/phones the site masks for free users.

⚠️ Proxy requirement

ImportYeti's Cloudflare edge blocks datacenter IPs on sight. The input schema defaults to residential US proxies and that is the configuration this actor is tested against. Leave the default alone unless you know exactly what you're doing.

🔄 How it works

1. Every entry in companies, suppliers, hsCodes, locations, and startUrls is normalized into a target URL. Bare HS codes resolve through the site's own redirect, so 8471 works as well as the full slugged URL.
2. Each target page is fetched once through residential US proxies with browser-grade TLS fingerprints, retrying across rotating IPs on failure.
3. The page's embedded JSON payload is extracted — this carries the full 50-row shipment table plus every rollup the page knows about, including fields the visible HTML never renders.
4. Records are mapped in a stable order per target — summary → bill-of-lading rows → top partners → trade lanes — and streamed to the dataset as each target completes.
5. If maxItems cuts the run short, the remaining work is encoded into a resumeCursor in the run OUTPUT. Start a new run with that cursor to continue without duplicate records or duplicate charges.

⚙️ Input parameters

Field	Type	Default	Description
companies	array	[]	US importer (consignee) slugs or URLs
suppliers	array	[]	Foreign supplier (shipper) slugs or URLs
hsCodes	array	[]	HS codes — 2-digit chapter, 4-digit heading, or 6-digit subheading
locations	array	[]	Country slugs or full /location/... URLs
locationType	string	supplier	Whether bare country slugs target the supplier or company directory
startUrls	array	[]	Any importyeti.com URLs, auto-classified
includeBols	boolean	true	Emit per-shipment bill_of_lading rows (up to 50 per target)
includeSummary	boolean	true	Emit one summary record per target
includeTopPartners	boolean	false	Emit top_partner records from the Top Suppliers / Top Companies tables
includeTradeLanes	boolean	false	Emit trade_lane port-pair records
resumeCursor	string	—	Cursor from a previous run's OUTPUT to continue where it stopped
maxItems	integer	1000	Hard cap on total records across all targets and record types
maxConcurrency	integer	10	Target pages fetched in parallel
proxy	object	residential US	Apify proxy configuration — residential US required

📊 Output overview

All record types ride in the same dataset and share a common header — record_type, target_type, target_slug, target_url, target_name, source_url, scraped_at — so one record_type filter cleanly splits the output into four flat tables. A typical company target with everything enabled yields 1 summary + 50 BOL rows + up to 50 top partners + its trade lanes.

📦 Output samples

bill_of_lading — one per shipment row:

{
    "record_type": "bill_of_lading",
    "target_type": "company",
    "target_slug": "apple",
    "target_url": "https://www.importyeti.com/company/apple",
    "target_name": "Apple",
    "arrival_date": "2026-01-21",
    "bill_of_lading": "SHPT012025121832",
    "bill_type": "H",
    "house_bol": "SHPT012025121832",
    "master_bol": "ZIMUBCN10198564",
    "carrier_scac": "ZIMU",
    "counterparty_name": "Galan Textile Machinery Sl",
    "counterparty_address": "Calle Francisco Oller 91 Barcelona Es",
    "counterparty_city": "Barcelona",
    "counterparty_country": "Spain",
    "counterparty_country_code": "ES",
    "counterparty_slug": "galan-textile-machinery",
    "counterparty_url": "https://www.importyeti.com/supplier/galan-textile-machinery",
    "weight_kg": 690,
    "quantity": 2,
    "quantity_unit": "pkg",
    "container_count": 1,
    "product_description": "Double Twisting Machine Bobbins First Twist Overhead Creel Winding Machine",
    "shipping_route": "Eu,Transatlantic",
    "est_freight_cost_usd": 23.49,
    "source_url": "https://www.importyeti.com/company/apple",
    "scraped_at": "2026-07-10T22:45:21.048Z"
}

summary — one per target (trimmed):

{
    "record_type": "summary",
    "target_type": "company",
    "target_slug": "apple",
    "target_name": "Apple",
    "total_shipments": 2443,
    "first_shipment_date": "2015-03-25",
    "most_recent_shipment_date": "2026-01-21",
    "website": "apple.com",
    "also_known_as": ["Apple Aps", "Apple Inc"],
    "hs_codes": [
        { "code": "85", "description": "Electric motors/Appliances", "shipments": 1186, "weight_kg": 5464266, "teu": 2010 }
    ],
    "top_carriers": [
        { "scac": "FTNV", "name": "Fedex Trade Networks Transport & Brokerage", "shipments": 1666 }
    ],
    "bill_type_breakdown": { "house": 2439, "regular": 3, "in_bond_regular": 1 },
    "uflpa_flag": false,
    "trademarks_count": 1620
}

top_partner — one per row of the Top Suppliers / Top Companies tables (trimmed):

{
    "record_type": "top_partner",
    "partner_type": "supplier",
    "partner_name": "Hon Hai Precision Industrial",
    "partner_address": "10Th Indl Dist N 2 Donghuan Rd Long Hua Baoan Shenzhen Gd Cn",
    "partner_country": "China",
    "origin_country": "Hong Kong S.A.R.",
    "total_weight_kg": 3373940,
    "total_teu": 1491,
    "hs_codes": ["8504.40", "8517.62", "8471.30"],
    "top_counterparties": [
        { "name": "Amazon Services", "shipments_12m": 770, "total_shipments": 1548 }
    ]
}

trade_lane — one per port pair:

{
    "record_type": "trade_lane",
    "exit_port": "Hong Kong",
    "exit_port_country": "Hong Kong S.A.R.",
    "entry_port": "Los Angeles, Ca",
    "entry_port_region": "California",
    "shipments": 607,
    "weight_kg": 2092579,
    "teu": 1021,
    "est_shipping_cost_usd": 519044
}

🗂 Key output fields

Group	Fields	Notes
Target context	record_type, target_type, target_slug, target_url, target_name	On every record — filter and join on these
BOL identity	bill_of_lading, bill_type, house_bol, master_bol, carrier_scac	SCAC derived from the master BOL prefix
Counterparty	counterparty_name, counterparty_address, counterparty_city, counterparty_country, counterparty_url	Supplier when the target is an importer; customer when the target is a supplier
Shipment	arrival_date, weight_kg, quantity, quantity_unit, container_count, product_description, shipping_route, est_freight_cost_usd	Freight cost is ImportYeti's estimate; null when the site has none
Summary rollups	total_shipments, first/most_recent_shipment_date, hs_codes[], hts_codes[], top_carriers[], bill_type_breakdown, website, uflpa_flag	Company/supplier summaries are the richest
Partner tables	partner_name, partner_address, origin_country, total_shipments, total_weight_kg, total_teu, hs_codes[], top_counterparties[]	Supplier discovery without fetching every profile
Trade lanes	exit_port, entry_port, entry_port_region, shipments, weight_kg, teu, est_shipping_cost_usd	Port-pair rollups per target

❓ FAQ

How do I scrape ImportYeti? Paste company slugs (apple), supplier slugs, HS codes (8471), country slugs (china), or any importyeti.com URL. Every input is normalized and classified automatically — slugs, paths, and full URLs all work interchangeably.

Can I get more than 50 shipments per company? No — 50 recent shipments per target is what ImportYeti exposes publicly, and this actor doesn't bypass their paid tier. What you can do is widen coverage: target the company and its top suppliers and the relevant HS codes in one run, which triangulates far more of the picture than any single page.

What's the difference between top_partner and fetching each supplier page? top_partner records come from the target page's own league table — up to 50 partners with addresses, volumes, HS codes, and their top counterparties, for the price of the one page you already fetched. Fetch individual supplier pages only for the partners you actually care about.

How does the resume cursor work? When maxItems stops a run early, the OUTPUT tab contains a resumeCursor string encoding exactly which targets (and how many records per target) are still owed. Start a new run with that cursor in the resumeCursor field — delivery continues from that point, so you never pay twice for the same record.

Does this scraper need proxies? Yes — residential US. ImportYeti's Cloudflare edge blocks datacenter IPs. The default configuration already has this right; don't switch it off.

Are emails and phone numbers included? Company websites are included when ImportYeti publishes them, and supplier phone numbers when the site exposes them publicly. Emails and masked phone numbers behind ImportYeti's premium tier are not included — the site redacts them server-side and this actor doesn't pretend otherwise.

💬 Support

Found an issue or need a field this actor doesn't emit yet? Open an issue on the actor's Issues tab in Apify Console — issues are typically answered within a day.

🛠 Additional services

Need a custom extraction pipeline, another trade-data source (Panjiva-style datasets, port statistics, customs registries), or this data delivered to your warehouse on a schedule? Get in touch via the Issues tab.

🔎 Explore more scrapers

More scrapers by the same author are available on the memo23 Apify profile — 270+ actors covering business directories, real-estate portals, job boards, and lead-generation sources.

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⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by ImportYeti, LLC or any of its subsidiaries or affiliates. All trademarks mentioned are the property of their respective owners.

The scraper accesses only publicly available import-record pages — no authenticated endpoints, premium-tier features, or content behind an ImportYeti login. US bill-of-lading data is public information published by CBP. Users are responsible for ensuring their use complies with ImportYeti's Terms of Service, applicable data-protection law (GDPR, CCPA, etc.), and any contractual obligations of their own organisation.

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